In order to improve the accuracy and the Out Of Vocabulary (OOV) recognition rate of the Chinese word segmentation, a Chinese word segmentation system based on character representation learning method was proposed. Firstly, the word in the text was mapped to a vector in a high-dimentioanl vecter space using Skip-gram model; then the K-means clustering algorithm was used to acquire clusters of the word vector, and the clustering results were regarded as features of Conditional Random Fields (CRF) model for training. Finally the CRF model was used for word segmentation and OOV recognition. The influences of the word vector dimensions, the number of clusters and different cluster algorithm on word segmentation were analyzed. Experiments were conducted on the 4th CCF Conference on Natural Language Processing & Chinese Computing (NLPCC2015) corpus. Experimental results show that the proposed system can effectively improve Chinese short text segmentation performance without using external knowledge, the F-value and the OOV recognition rate achieve to 95.67% and 94.78% respectively.